Study region: This study focuses on Chaharmahal and Bakhtiari Province, a semi-arid, mountainous area in western Iran. This region is recognized as one of the country's most hydrologically complex and climatically extreme zones, making it a compelling setting for investigating flood susceptibility and water resource challenges. Study focus: This study aims to expand the application of reinforcement learning (RL) from flood management to flood susceptibility mapping by integrating RL algorithms with geographic information systems (GIS). We implemented Q-Learning (OL), Proximal Policy Optimization (PPO)based Proximal Updating (PU), and Deep Q-Learning (DQL), and introduced RL-Stack. To ensure interpretability, we applied SHAP for variable attribution. Validated through a real-world case study, the methodology delivered accurate, actionable maps to support resilient flood risk management. New hydrological insights: The PU model most effectively captured flood susceptibility, balancing sensitivity to flood-prone areas with stability across heterogeneous landscapes. DQL showed overestimation bias and unstable local predictions. RL-Stack improved the detection of highly susceptible zones, while traditional QL performed poorly in continuous, imbalanced settings. The SHAP-based analysis identified snow depth as the primary hydrological control, exhibiting a dual role: deep snowpacks can intensify floods during rapid melt or attenuate runoff when retained. Short-duration, intense rainfall strongly interacted with snow depth and flow accumulation, generating disproportionately large floods through rain-on-snow processes.